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https://github.com/ashim-hq/ashim
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85b1cfc10a
Complete rebrand from Stirling-Image to ashim following the project move to https://github.com/ashim-hq/ashim. Changes across 117 files: - Package scope: @stirling-image/* → @ashim/* - GitHub URLs: stirling-image/stirling-image → ashim-hq/ashim - Docker Hub: stirlingimage/stirling-image → ashimhq/ashim - GitHub Pages: stirling-image.github.io → ashim-hq.github.io - All branding text: "Stirling Image" → "ashim" - Docker service/volumes/user: stirling → ashim - Database: stirling.db → ashim.db - localStorage keys: stirling-token → ashim-token - Environment variables: STIRLING_GPU → ASHIM_GPU - Python cache dirs: .cache/stirling-image → .cache/ashim - SVG filter IDs, test prefixes, and all other references
30 lines
929 B
Python
30 lines
929 B
Python
"""Runtime GPU/CUDA detection utility."""
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import functools
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import os
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@functools.lru_cache(maxsize=1)
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def gpu_available():
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"""Return True if a usable CUDA GPU is present at runtime."""
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# Allow explicit disable via env var (set to "false" or "0")
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override = os.environ.get("ASHIM_GPU")
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if override is not None and override.lower() in ("0", "false", "no"):
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return False
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# Use torch.cuda as the source of truth. It actually probes
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# the hardware. onnxruntime's get_available_providers() only
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# reports compiled-in backends, not whether a GPU exists.
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try:
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import torch
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return torch.cuda.is_available()
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except ImportError:
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pass
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return False
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def onnx_providers():
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"""Return ONNX Runtime execution providers in priority order."""
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if gpu_available():
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return ["CUDAExecutionProvider", "CPUExecutionProvider"]
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return ["CPUExecutionProvider"]
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